Numerical Study on Heat Transfer Characteristic of Hot Metal Transportation before EAF Steelmaking Process

被引:0
|
作者
Chen, Weizhen [1 ]
Hu, Hang [1 ]
Wang, Shuai [1 ]
Chen, Feng [1 ]
Guo, Yufeng [1 ]
Yang, Lingzhi [1 ]
机构
[1] Cent South Univ, Sch Minerals Proc & Bioengn, Changsha 410083, Peoples R China
基金
中国国家自然科学基金;
关键词
numerical simulation; heat transfer characteristic; temperature drop; hot metal transportation; slag thickness; STEEL; FLOW;
D O I
10.3390/met14060673
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
The temperature of hot metal (HM) is crucial for the energy input and smelting in the electric arc furnace (EAF) steelmaking process with HM and scrap as the charge structure. However, due to the influence of many factors in the heat dissipation in HM transportation before the EAF steelmaking process, the temperature drop of HM before charged is usually fluctuating and uncertain. This situation is not conducive to the input energy control and energy optimization of the EAF steelmaking process. In this paper, a three-dimensional numerical model of a 90-ton hot metal ladle is established to simulate the heat transfer characteristic of HM transportation through ANSYS Fluent 2023 and verified by on-the-spot testing and sample analysis. The effects of ambient temperature, air velocity, slag thickness and furnace cover thickness on the temperature drop of HM are investigated and quantitatively analyzed in 30 numerical schemes. The results indicate that slag thickness is the most influential factor, followed by furnace cover thickness, air velocity and ambient temperature. In the case of 50 min transport time, the temperature drop of HM is 55.2, 15.06, 12.08, 10.38, 10.29 and 10.26 degrees C when the slag thickness is 0, 50, 100, 150, 200 and 250 mm, respectively. While HM is not covered by slag, the furnace cover can also greatly reduce the temperature drop. Based on the simulated data, a prediction model of HM temperature drop is obtained through the multi-factor coupling analysis and mathematical fitting. This study can help develop targeted insulation measures and determine the temperature of HM, which is expected to control the input energy for deep energy-saving optimization in the EAF steelmaking process.
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页数:19
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